Time series analysis of climate variables using seasonal ARIMA approach
Time series analysis of climate variables using seasonal ARIMA approach
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DOI：
10.1007/s12040-020-01408-x
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发表时间：
2020-06-27
影响因子：
1.9
通讯作者：
Sharif, Mohammad
中科院分区：
文献类型：
--
作者：
Dimri, Tripti;Ahmad, Shamshad;Sharif, Mohammad
气候的动态结构受降水和温度变化的支配，可以通过对这些因素的时间序列分析来研究。本文描述了印度北阿肯德邦Bhagirathi河流域月平均最低、最高气温和降水的时间序列调查和季节分析。使用的数据是1901-2000年(100年)。采用季节ARIMA(SARIMA)模型对未来20年(2001-2020年)进行预测。自回归(P)整合(D)移动平均(Q)(ARIMA)模型基于Box Jenkins方法，通过使数据平稳并去除季节性来预测未来趋势。结果表明，对降水数据进行时间序列分析最合适的模型是SARIMA(0，1，1)(0，1，1)(12)(常量)，对温度数据进行时间序列分析的最合适模型是SARIMA(0，1，0)(0，1，1)(12)(常量)。模型预测结果表明，预测数据与数据中的趋势吻合较好。然而，在极端降雨事件和温度结果中发现了过度预测。模式和趋势的信息可以作为在该地区制定更好的水管理做法的预测工具。
The dynamic structure of climate is governed by changes in precipitation and temperature and can be studied by time series analysis of these factors. This paper describes investigation of time series and seasonal analysis of the monthly mean minimum and maximum temperatures and the precipitation for the Bhagirathi river basin situated in the state of Uttarakhand, India. The data used is from the year 1901-2000 (100 years). The seasonal ARIMA (SARIMA) model was used and forecasting was done for next 20 years (2001-2020). The auto-regressive (p) integrated (d) moving average (q) (ARIMA) model is based on Box Jenkins approach which forecasts the future trends by making the data stationary and removing the seasonality. It was found that the most appropriate model for time series analysis of precipitation data was SARIMA(0,1,1) (0,1,1)(12)(with constant) and of temperature data was SARIMA(0,1,0) (0,1,1)(12)(with constant). The model prediction results show that the forecast data fits well with the trend in the data. However, over-predictions are found in extreme rainfall events and temperature results. The information of pattern and trends can assist as a prediction tool for development of better water management practices in the area.